It is becoming increasingly clear that combining multi-modal；brain；imaging；data；is able；to；provide more information for individual subjects by exploiting；the；rich；multimodal；information that exists. However,；the；number；of；studies that do true；multimodal；fusion；(i.e. capitalizing on joint information among modalities) is still remarkably small given；the；known benefits.；In；part, this is because multi-modal studies require broader expertise；in；collecting, analyzing, and interpreting；the；results than do unimodal studies.；In；this paper, we start by introducing；the；basic reasons why；multimodal；data；fusion；is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect；brain；imaging；studies. We also discuss；the；challenges that need；to；be confronted for such approaches；to；be more widely applied by；the；community. We then provide；areview；of；the；diverse studies that have used；multimodal；data；fusion；(primarily focused on psychosis) as well as provide an introduction；to；some；of；the；existing analytic approaches. Finally, we discuss some up-and-coming approaches；to；multi-modal；fusion；including deep learning and；multimodalclassification which show considerable promise. Our conclusion is that；multimodal；data；fusion；is rapidly growing, but it is still underutilized.；The；complexity；of；the；human；brain；coupled with；theincomplete measurement provided by existing；imaging；technology makes；multimodal；fusion；essential；in；order；to；mitigate against misdirection and hopefully provide；a；key；to；finding；the；missing；link(s)；incomplex；mental；illness.